[en] Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.
Disciplines :
Neurosciences & behavior
Author, co-author :
Li, Jingwei ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center ; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University ; Department of Electrical and Computer Engineering, Centre for Sleep and Cognition
Bzdok, Danilo ; Department of Biomedical Engineering, Montreal Neurological Institute (MNI), ; Mila-Quebec Artificial Intelligence Institute, Montreal, QC, Canada.
Chen, Jianzhong ; Department of Electrical and Computer Engineering, Centre for Sleep and Cognition
Tam, Angela ; Department of Electrical and Computer Engineering, Centre for Sleep and Cognition
Ooi, Leon Qi Rong ; Department of Electrical and Computer Engineering, Centre for Sleep and Cognition
Holmes, Avram J ; Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA. ; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine
Ge, Tian; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, ; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, ; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School,
Patil, Kaustubh R ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center ; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University
Jabbi, Mbemba; Department of Psychiatry, Dell Medical School, University of Texas at Austin, ; The Mulva Clinic for Neurosciences, Dell Medical School, University of Texas at ; Institute of Neuroscience, University of Texas at Austin, Austin, TX, USA. ; Department of Psychology, University of Texas at Austin, Austin, TX, USA.
Eickhoff, Simon B ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center ; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University
Yeo, B T Thomas ; Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, ; Integrative Sciences and Engineering Programme (ISEP), National University of
Genon, Sarah ; Université de Liège - ULiège > Département des sciences cliniques > Neuroimagerie des troubles de la mémoire et revalidation cognitive ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center ; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University
Language :
English
Title :
Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.
Publication date :
18 March 2022
Journal title :
Science Advances
eISSN :
2375-2548
Publisher :
American Association for the Advancement of Science (AAAS), Washington, Us dc
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